Identification, organisation and visualisation of complete proteomes in UniProt throughout all taxonomic ranks :|barchaea, bacteria, eukatyote and virus

Users of uniprot.org want to be able to query, retrieve and download proteome sets for an organism of their choice. They expect the data to be easily accessed, complete and up to date based on current available knowledge. UniProt release 2012_01 (25th Jan 2012) contains the proteomes of 2,923 organisms; 50% of which are bacteria, 38% viruses, 8% eukaryota and 4% archaea. Note that the term 'organism' is used in a broad sense to include subspecies, strains and isolates. Each completely sequenced organism is processed as an independent organism, hence the availability of 38 strain-specific proteomes Escherichia coli that are accessible for download. There is a project within UniProt dedicated to the mammoth task of maintaining the “Proteomes database”. This active resource is essential for UniProt to continually provide high quality proteome sets to the users. Accurate identification and incorporation of new, publically available, proteomes as well as the maintenance of existing proteomes permits sustained growth of the proteomes project. This is a huge, complicated and vital task accomplished by the activities of both curators and programmers. This thesis explains the data input and output of the proteomes database: the flow of genome project data from the nucleotide database into the proteomes database, then from each genome how a proteome is identified, augmented and made visible to uniprot.org users. Along this journey of discovery many issues arose, puzzles concerning data gathering, data integrity and also data visualisation. All were resolved and the outcome is a well-documented, actively maintained database that strives to provide optimal proteome information to its users

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

The organization and mining of malaria genomic and post-genomic data is highly motivated by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should therefore be as reliable and versatile as possible. In this context, we examined five aspects of the organization and mining of malaria genomic and post-genomic data: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from X-omic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Progresses toward a grid-enabled chemogenomic knowledge space are discussed.Comment: 43 pages, 4 figures, to appear in Malaria Journa

Accurate reconstruction of bacterial pan- and core genomes with PEPPAN

Bacterial genomes can contain traces of a complex evolutionary history, including extensive homologous recombination, gene loss, gene duplications and horizontal gene transfer. In order to reconstruct the phylogenetic and population history of a set of multiple bacteria, it is necessary to examine their pangenome, the composite of all the genes in the set. Here we introduce PEPPAN, a novel pipeline that can reliably construct pangenomes from thousands of genetically diverse bacterial genomes that represent the diversity of an entire genus. PEPPAN outperforms existing pangenome methods by providing consistent gene and pseudogene annotations extended by similarity-based gene predictions, and identifying and excluding paralogs by combining tree- and synteny-based approaches. The PEPPAN package additionally includes PEPPAN_parser, which implements additional downstream analyses including the calculation of trees based on accessory gene content or allelic differences between core genes. In order to test the accuracy of PEPPAN, we implemented SimPan, a novel pipeline for simulating the evolution of bacterial pangenomes. We compared the accuracy and speed of PEPPAN with four state-of-the-art pangenome pipelines using both empirical and simulated datasets. PEPPAN was more accurate and more specific than any of the other pipelines and was almost as fast as any of them. As a case study, we used PEPPAN to construct a pangenome of ~40,000 genes from 3052 representative genomes spanning at least 80 species of Streptococcus. The resulting gene and allelic trees provide an unprecedented overview of the genomic diversity of the entire Streptococcus genus

HaMStR: Profile hidden markov model based search for orthologs in ESTs

BACKGROUND: EST sequencing is a versatile approach for rapidly gathering protein coding sequences. They provide direct access to an organism's gene repertoire bypassing the still error-prone procedure of gene prediction from genomic data. Therefore, ESTs are often the only source for biological sequence data from taxa outside mainstream interest. The widespread use of ESTs in evolutionary studies and particularly in molecular systematics studies is still hindered by the lack of efficient and reliable approaches for automated ortholog predictions in ESTs. Existing methods either depend on a known species tree or cannot cope with redundancy in EST data. RESULTS: We present a novel approach (HaMStR) to mine EST data for the presence of orthologs to a curated set of genes. HaMStR combines a profile Hidden Markov Model search and a subsequent BLAST search to extend existing ortholog cluster with sequences from further taxa. We show that the HaMStR results are consistent with those obtained with existing orthology prediction methods that require completely sequenced genomes. A case study on the phylogeny of 35 fungal taxa illustrates that HaMStR is well suited to compile informative data sets for phylogenomic studies from ESTs and protein sequence data. CONCLUSION: HaMStR extends in a standardized manner a pre-defined set of orthologs with ESTs from further taxa. In the same fashion HaMStR can be applied to protein sequence data, and thus provides a comprehensive approach to compile ortholog cluster from any protein coding data. The resulting orthology predictions serve as the data basis for a variety of evolutionary studies. Here, we have demonstrated the application of HaMStR in a molecular systematics study. However, we envision that studies tracing the evolutionary fate of individual genes or functional complexes of genes will greatly benefit from HaMStR orthology predictions as well

Clustering of cognate proteins among distinct proteomes derived from multiple links to a single seed sequence

<p>Abstract</p> <p>Background</p> <p>Modern proteomes evolved by modification of pre-existing ones. It is extremely important to comparative biology that related proteins be identified as members of the same cognate group, since a characterized putative homolog could be used to find clues about the function of uncharacterized proteins from the same group. Typically, databases of related proteins focus on those from completely-sequenced genomes. Unfortunately, relatively few organisms have had their genomes fully sequenced; accordingly, many proteins are ignored by the currently available databases of cognate proteins, despite the high amount of important genes that are functionally described only for these incomplete proteomes.</p> <p>Results</p> <p>We have developed a method to cluster cognate proteins from multiple organisms beginning with only one sequence, through connectivity saturation with that Seed sequence. We show that the generated clusters are in agreement with some other approaches based on full genome comparison.</p> <p>Conclusion</p> <p>The method produced results that are as reliable as those produced by conventional clustering approaches. Generating clusters based only on individual proteins of interest is less time consuming than generating clusters for whole proteomes. </p

Pan-genome Analysis, Visualization and Exploration

The dynamics of prokaryotic genomes are driven by the intricate interplay of different evolutionary forces such as gene duplication, gene loss and horizontal transfer. Even closely related strains can exhibit remarkable genetic diversity and substantial gene presence/absence variation. The pan-genome, namely the complete inventory of genes in a collection of strains, can be several times larger than the genome of any single strain. Although several tools for pan-genome analysis have been published, there is still much room for algorithmic improvement, as well as needs for applications that better interactively visualize and explore pan-genomes. Therefore, we have developed panX, an automated computational pipeline for efficient identification of orthologous gene clusters in the pan-genome. PanX identifies homologous relationships among genes using DIAMOND and MCL and then harnesses phylogeny-based post- processing to separate orthologs from paralogs. Furthermore, we take advantage of a divide-and-conquer strategy to achieve an approximately linear runtime on large datasets. The analysis result can be visualized by the accompanying software, an easy-to-use and powerful web-based visualization application for interactive exploration of the pan-genome. The visualization dashboard encompasses a variety of connected components that allow rapid searching, filtering and sorting of genes and flexible investigation of evolutionary relationships among strains and their genes. PanX seamlessly interlinks gene clusters with their alignments and gene phylogenies, maps mutations on the branches of gene tree and highlights gene gain and loss events on the core-genome phylogeny that can also be colored by metadata associated with strains. By using 120 simulated pan-genome datasets for benchmarking and comparing clustering results on real dataset between different tools, panX exhibits overall good performance across a large range of diversities. PanX is available at pangenome.de, with a wide range of microbial pan-genomes established. Besides, user-provided pan-genomes can be visualized either via a web server or by running panX locally as a web-based application

Identification and Analysis of Genes and Pseudogenes within Duplicated Regions in the Human and Mouse Genomes

The identification and classification of genes and pseudogenes in duplicated regions still constitutes a challenge for standard automated genome annotation procedures. Using an integrated homology and orthology analysis independent of current gene annotation, we have identified 9,484 and 9,017 gene duplicates in human and mouse, respectively. On the basis of the integrity of their coding regions, we have classified them into functional and inactive duplicates, allowing us to define the first consistent and comprehensive collection of 1,811 human and 1,581 mouse unprocessed pseudogenes. Furthermore, of the total of 14,172 human and mouse duplicates predicted to be functional genes, as many as 420 are not included in current reference gene databases and therefore correspond to likely novel mammalian genes. Some of these correspond to partial duplicates with less than half of the length of the original source genes, yet they are conserved and syntenic among different mammalian lineages. The genes and unprocessed pseudogenes obtained here will enable further studies on the mechanisms involved in gene duplication as well as of the fate of duplicated genes

A novel approach to infer orthologs and produce gene annotations at scale

Aufgrund von Fortschritten im Bereich der DNA-Sequenzierung hat die Anzahl verfügbarer Genome in den letzten Jahrzehnten rapide zugenommen. Tausende bereits heute zur Verfügung stehende Genome ermöglichen detaillierte vergleichende Analysen, welche für die Beantwortung relevanter Fragestellungen essentiell sind. Dies betrifft die Assoziation von Genotyp und Phänotyp, die Erforschung der Besonderheiten komplexer Proteine und die Weiterentwicklung medizinischer Anwendungen. Um all diese Fragen zu beantworten ist es notwendig, proteinkodierende Gene in neu sequenzierten Genomen zu annotieren und ihre Homologieverhältnisse zu bestimmen. Die bestehenden Methoden der Genomanalyse sind jedoch nicht für Menge heutzutage anfallender Datenmengen ausgelegt. Daher ist die zentrale Herausforderung in der vergleichenden Genomik nicht die Anzahl der verfügbaren Genome, sondern die Entwicklung neuer Methoden zur Datenanalyse im Hochdurchsatz. Um diese Probleme zu adressieren, schlage ich ein neues Paradigma der Annotation von Genomen und der Inferenz von Homologieverhältnissen vor, welches auf dem Alignment gesamter Genome basiert. Während die derzeit angewendeten Methoden zur Gen-Annotation und Bestimmung der Homologie ausschließlich auf codierenden Sequenzen beruhen, könnten durch die Einbeziehung des umgebenden neutral evolvierenden genomischen Kontextes bessere und vollständigere Annotationen vorgenommen werden. Die Verwendung von Genom-Alignments ermöglicht eine beliebige Skalierung der vorgeschlagenen Methodik auf Tausende Genome. In dieser Arbeit stelle ich TOGA (Tool to infer Orthologs from Genome Alignments) vor, eine bioinformatische Methode, welche dieses Konzept implementiert und Homologie- Klassifizierung und Gen-Annotation in einer einzelnen Pipeline kombiniert. TOGA verwendet Machine-Learning, um Orthologe von Paralogen basierend auf dem Alignment von intronischer und intergener Regionen zu unterscheiden. Die Ergebnisse des Benchmarkings zeigen, dass TOGA die herkömmlichen Ansätze innerhalb der Placentalia übertrifft. TOGA klassifiziert Homologieverhältnisse mit hoher Präzision und identifiziert zuverlässig inaktivierte Gene als solchet. Frühere Versionen von TOGA fanden in mehreren Studien Anwendung und wurden in zwei Publikationen verwendet. Außerdem wurde TOGA erfolgreich zur Annotation von 500 Säugetiergeenomen verwendet, dies ist der bisher umfangreichste solche Datensatz. Diese Ergebnisse zeigen, dass TOGA das Potenzial hat, sich zu einer etablierten Methode zur Gen-Annotation zu entwickeln und die derzeit angewandten Techniken zu ergänzen

Automatically extracting functionally equivalent proteins from SwissProt

In summary, FOSTA provides an automated analysis of annotations in UniProtKB/Swiss-Prot to enable groups of proteins already annotated as functionally equivalent, to be extracted. Our results demonstrate that the vast majority of UniProtKB/Swiss-Prot functional annotations are of high quality, and that FOSTA can interpret annotations successfully. Where FOSTA is not successful, we are able to highlight inconsistencies in UniProtKB/Swiss-Prot annotation. Most of these would have presented equal difficulties for manual interpretation of annotations. We discuss limitations and possible future extensions to FOSTA, and recommend changes to the UniProtKB/Swiss-Prot format, which would facilitate text-mining of UniProtKB/Swiss-Prot